A Bayesian Hierarchical Modeling Framework for Geospatial Analysis of Adverse Pregnancy Outcomes

Study describing a Bayesian hierarchichal modeling framework used to explore which neighborhood-level factors and patient-level features were most informative for preterm birth and stillbirth pregnancy outcomes.

Individual-Level and Neighborhood-Level Risk Factors for Severe Maternal Morbidity

Study showing that neighborhood-level risk factors are independent predictors of Severe Maternal Morbidity, providing further evidence that racial disparities in maternal outcomes are symptoms of historical and structural racism.

Not All C-sections Are the Same: Investigating Emergency vs. Elective C-section Deliveries as an Adverse Pregnancy Outcome

Publication and poster accepted for the 2021 Pacific Biocomputing Symposium. This study utilizes Electronic Health Record (EHR) data to assess the impact of pregnancy-specific maternal morbidity and patient-specific characteristics on experiencing an emergency admission at the time of delivery and its relationship to Cesarean section (C-section) deliveries

Development and Evaluation of MADDIE: Method to Acquire Delivery Date Information from Electronic Health Records

An R algorithm designed to extract delivery episode details from structured Electronic Health Record data.

Is Hydroxychloroquine Safe During Pregnancy? Observations from Penn Medicine

Preprint as a contribution from a rapid response exploration of hydroxychloroquine effects on pregnancy-related outcomes.